In recent years, smart computing has emerged as a promising and rapidly expanding field of technology. It senses the environment in real time and gives powerful analytics to perform intelligent decisions. Creating a scheduling algorithm based on priorities in order to decrease IoT process latency was the primary emphasis of the study challenge. The constraints of existing scheduling algorithms were investigated in order to build a scheduling algorithm that is based on priorities. We provide a context-based priority scheduling method to get around these restrictions. In order to determine which steps of the IoT process were crucial, we developed context attributes. Once the criticality has been identified, the proposed scheduling technique is used to schedule the IoT processes. The outcomes of the algorithms were confirmed using a variety of evaluation indicators. As demonstrated by the experimental results, the suggested scheduling algorithms outperformed the state-of-the-art techniques. Smart ATM uses a Case Study technique to analyse the algorithm. We identified the sensors that are part of the ATM and the settings in which they are relevant. We determined the priority value for each sensor. The processes are subsequently categorized according to their priority values. Then, a priority-based FCFS scheduling algorithm is used, and its performance is assessed using metrics like Average TAT, Cost, Energy, and the High critical process TAT ratio.

A number of disadvantages of traditional networks may be attributed to the close relationship that exists between the control plane and the data plane inside proprietary hardware designs, as described above. The problem of security is one of the most difficult to deal with. There are a plethora of network hazards and attacks that might be encountered these days. DDoS attacks are one of the most popular and disruptive attacks on the internet today, and they affect a wide range of organisations. Despite a large number of traditional mitigation solutions now available, the frequency, volume, and intensity of distributed denial‐of‐service (DDoS) attacks continue to rise. According to the findings of this paper, a new network paradigm is necessary to satisfy the requirements of today’s complex security concerns. It was necessary to develop a software‐defined network (SDN) in order to meet the real‐time needs of the massive network that was expanding at an exponential rate. Many advantages of SDN exist, including simplicity of administration, scalability, and agility, but one of the most critical is security, which is one of the most important considerations when implementing SDN. SDS may be seen as a paradigm in which the implementation of new security regulations in the computer environment is performed via the use of protected software, which is described further below. The goal is to provide a flexible and extensible architecture for DDoS detection and prevention that is both flexible and extendable; the suggested clustering approach, which is based on the Open Day Light (ODL) Controller, is employed to carry out the experimental findings. In this section, we emphasise DDoS penetration techniques from a range of tools, and we evaluate the vulnerability against various tactics. It is necessary to use a Mininet emulation tool to construct a detection and prevention system against distributed denial of service (DDoS) attacks in order to achieve success. There is a range of other simulation tools that are utilised in conjunction with this research in order to bring it to a conclusion. Integration of industry standards such as SNORT and Flow has been accomplished in a variety of situations and parameter settings. During the creation of a framework capable of detecting and mitigating DDoS attacks at an early stage in both the control and application levels, the implementation of this framework has been shown to be crucial in the development of a framework.

Breast cancer is responsible for the deaths of the vast majority of women who succumb to the disease. By detecting and treating the disease at an earlier stage, it is feasible to reduce the mortality rate associated with breast cancer. Mammography is the technique of breast cancer screening that has received the most amount of approval from the medical community. Imaging of the ipsilateral projections of the bilateral (right and left) breasts, also known as mediolateral oblique (MLO) and craniocaudal (CC) views, is often necessary for this surgery. This imaging technique is also known as the craniocaudal projection. Sonography, which is also known as ultrasound imaging, is used in combination with mammography during the diagnostic phase (which occurs after the screening phase) to offer a more accurate examination of any abnormalities that may have been detected. Radiologists may be able to make a more precise diagnosis of breast cancer by carrying out an objective assessment with the assistance of CAD systems. CAD is an abbreviation that stands for computer-aided detection and diagnosis. Researchers have proposed computer-aided design (CAD) systems as a viable technique for increasing system performance. These CAD systems take information from a variety of sources and combine it into a single database. In the majority of occurrences, this necessitates the inclusion of qualities or evaluations that were collected from a wide range of information sources. Fusion of choices is effective when dealing with sources that are statistically independent, while fusion of characteristics is good when dealing with sources that have a significant degree of correlation with one another. However, sources often contain a mix of information that is associated with one another as well as information that is independent of one another; as a consequence, none of these approaches is the greatest choice available to choose from. The development of optimal fusion strategies for Multiview and multimodal breast CAD systems is the major focus of this thesis. Canonical correlation analysis is the name of the statistical approach that serves as the foundation for these tactics (CCA). The CCA algorithm alters two multivariate datasets in such a manner as to maximize the correlation that already exists between them. This, in turn, optimizes the feature fusion that occurs after the CCA method has been applied. On the other hand, the performance of benchmark fusion schemes that combine all three sources of information is only at most equivalent to the performance of benchmark schemes that fuse two information sources. In addition, the performance of benchmark fusion schemes that combine all three sources of information is worse than the performance of CCA-based feature fusion schemes that combine two sources of information. This indicates that even if increasing the number of sources could bring new information, only a fusion approach that is optimized to exploit its maximum potential would be able to make the most of this extra data. In conclusion, the CCA-based fusion schemes exhibit robustness when tested against a wide array of performance indicators, datasets, information sources, and diagnostic tasks that are related to the diagnosis of breast cancer. The benchmark fusion techniques, on the other hand, do not demonstrate this resilience.